Probability Theory¶
Jaynes, E. T. (2003). Probability Theory: The Logic of Science. Cambridge University Press.
Cited by¶
6 citations across 6 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Bayesian Updating
This sourceFoundational Bayesian epistemology: argues that the only access to a system's true state is through inferential reasoning over noisy data conditioned on a model of the noise — formalizing the epistemological asymmetry between observation and reality.
- Epistemic Humility
- Epistemic humility encourages thinking in terms of epistemic position: Who knows what, with what warrant, and why?—the Bayesian framing of degrees of belief and conditional updating Jaynes (2003) develops as the logic of probability.
This sourceTreats probability as extended logic: degrees of belief updated by conditioning (Bayesian inference) as the consistent logic of plausible reasoning. SUPPORTS marker 221 (Bayesian framing of degrees of belief and conditional updating as the logic of probability).
- Epistemic humility encourages thinking in terms of epistemic position: Who knows what, with what warrant, and why?—the Bayesian framing of degrees of belief and conditional updating Jaynes (2003) develops as the logic of probability.
- Measurement Uncertainty and Observational Noise
- This creates epistemological asymmetry: we can never measure the true state, only infer it from noisy observations paired with a model of the noise, an inferential stance Jaynes (2003) develops in his treatment of probability as the logic of science.
This sourceFoundational Bayesian epistemology: argues that the only access to a system's true state is through inferential reasoning over noisy data conditioned on a model of the noise — formalizing the epistemological asymmetry between observation and reality.
- This creates epistemological asymmetry: we can never measure the true state, only infer it from noisy observations paired with a model of the noise, an inferential stance Jaynes (2003) develops in his treatment of probability as the logic of science.
- Stochasticity vs. Determinism
- In software testing, deterministic systems are debuggable (reproduce bugs reliably by setting initial conditions), whereas stochastic systems require statistical testing, Monte Carlo methods, and ensemble verification—a transfer Jaynes (2003) develops by treating probability as an extension of logic across domains.
This sourceFoundational Bayesian epistemology: argues that the only access to a system's true state is through inferential reasoning over noisy data conditioned on a model of the noise — formalizing the epistemological asymmetry between observation and reality.
- In software testing, deterministic systems are debuggable (reproduce bugs reliably by setting initial conditions), whereas stochastic systems require statistical testing, Monte Carlo methods, and ensemble verification—a transfer Jaynes (2003) develops by treating probability as an extension of logic across domains.
- Symmetric Response to Asymmetric State
- Bayesian inference. A uniform prior is not informationally neutral; over hypotheses whose prior evidence is asymmetric it pulls posteriors toward the under-evidenced hypothesis.
This sourceA uniform prior is not informationally neutral; over an evidentially asymmetric hypothesis space it allocates posterior mass toward the under-evidenced hypothesis, and stronger evidence is required to drive the posterior to where an evidence-weighted prior already sat.
- Bayesian inference. A uniform prior is not informationally neutral; over hypotheses whose prior evidence is asymmetric it pulls posteriors toward the under-evidenced hypothesis.
Domain-specific¶
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